Design space approach in the optimization of the spray-drying process

Abstract

From a quality by design perspective, the aim of the present study was to demonstrate the applicability of a Bayesian statistical methodology to identify the Design Space (DS) of a spray-drying process. Following the ICH Q8 guideline, the DS is defined as the "multidimensional combination and interaction of input variables (e.g., materials attributes) and process parameters that have been demonstrated to provide assurance of quality." Thus, a predictive risk-based approach was set up in order to account for the uncertainties and correlations found in the process and in the derived critical quality attributes such as the yield, the moisture content, the inhalable fraction of powder, the compressibility index, and the Hausner ratio. This allowed quantifying the guarantees and the risks to observe whether the process shall run according to specifications. These specifications describe satisfactory quality outputs and were defined a priori given safety, efficiency, and economical reasons. Within the identified DS, validation of the optimal condition was effectuated. The optimized process was shown to perform as expected, providing a product for which the quality is built in by the design and controlled setup of the equipment, regarding identified critical process parameters: the inlet temperature, the feed rate, and the spray flow rate.